How to Keep Your Neighbours in Order Conor McBride Abstract 2.

How to Keep Your Neighbours in Order
Conor McBride
University of Strathclyde
[email protected]
Abstract
2.
I present a datatype-generic treatment of recursive container types
whose elements are guaranteed to be stored in increasing order,
with the ordering invariant rolled out systematically. Intervals, lists
and binary search trees are instances of the generic treatment. On
the journey to this treatment, I report a variety of failed experiments
and the transferable learning experiences they triggered. I demonstrate that a total element ordering is enough to deliver insertion and
flattening algorithms, and show that (with care about the formulation of the types) the implementations remain as usual. Agda’s instance arguments and pattern synonyms maximize the proof search
done by the typechecker and minimize the appearance of proofs
in program text, often eradicating them entirely. Generalizing to
indexed recursive container types, invariants such as size and balance can be expressed in addition to ordering. By way of example, I
implement insertion and deletion for 2-3 trees, ensuring both order
and balance by the discipline of type checking.
If we intend to enforce invariants, we shall need to mix a little
bit of logic in with our types and a little bit of proof in with our
programming. It is worth taking some trouble to set up our logical
apparatus to maximize the effort we can get from the computer
and to minimize the textual cost of proofs. We should prefer to
encounter logic only when it is dangerously absent!
Our basic tools are the types representing falsity and truth by
virtue of their number of inhabitants:
1.
Introduction
It has taken years to see what was under my nose. I have been
experimenting with ordered container structures for a long time
[McBride(2000)]: how to keep lists ordered, how to keep binary
search trees ordered, how to flatten the latter to the former. Recently, the pattern common to the structures and methods I had often found effective became clear to me. Let me tell you about it.
Patterns are, of course, underarticulated abstractions. Correspondingly, let us construct a universe of container-like datatypes ensuring that elements are in increasing order, good for intervals, ordered
lists, binary search trees, and more besides.
This paper is a literate Agda program. The entire development
is available at https://github.com/pigworker/Pivotal. As
well as making the headline contributions
• a datatype-generic treatment of ordering invariants and opera-
tions which respect them
• a technique for hiding proofs from program texts
• a precise implementation of insertion and deletion for 2-3 trees
I take the time to explore the design space, reporting a selection of
the wrong turnings and false dawns I encountered on my journey to
these results. I try to extrapolate transferable design principles, so
that others in future may suffer less than I.
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c ACM [to be supplied]. . . $5.00
Copyright How to Hide the Truth
data 0 : Set where
-- no constructors!
record 1 : Set where constructor hi -- no fields!
Dependent types allow us to compute sets from data. E.g., we
can represent evidence for the truth of some Boolean expression
which we might have tested.
data 2 : Set where tt ff : 2
So : 2 → Set
So tt = 1
So ff = 0
A set P which evaluates to 0 or to 1 might be considered
‘propositional’ in that we are unlikely to want to distinguish its
inhabitants. We might even prefer not even to see its inhabitants.
I define a wrapper type for propositions whose purpose is to hide
proofs.
record pq (P : Set) : Set where
constructor !
field {{prf }} : P
Agda uses braces to indicate that an argument or field is to be
suppressed by default in program texts and inferred somehow by
the typechecker. Single-braced variables are solved by unification,
in the tradition of Milner. Doubled braces indicate instance arguments, inferred by contextual search: if just one hypothesis can take
the place of an instance argument, it is silently filled in, allowing us
a tiny bit of proof automation [Devriese and Piessens(2011)]. If an
inhabitant of pSo bq is required, we may write ! to indicate that we
expect the truth of b to be known.
Careful positioning of instance arguments seeds the context
with useful information. We may hypothesize over them quietly,
and support forward reasoning with a ‘therefore’ operator.
⇒ : Set → Set → Set
P ⇒ T = {{p : P }} → T
infixr 3 ⇒
∴ : ∀{P T } → pPq → (P ⇒ T ) → T
!∴t = t
This apparatus can give the traditional conditional a subtly more
informative type, thus:
¬ : 2 → 2; ¬ tt = ff; ¬ ff = tt
if then else :
∀{X } b → (So b ⇒ X ) → (So (¬ b) ⇒ X ) → X
if tt then t else f = t
if ff then t else f = f
infix 1 if then else
If ever there is a proof of 0 in the context, we should be able to
ask for anything we want. Let us define
magic : {X : Set} → 0 ⇒ X
magic {{()}}
using Agda’s absurd pattern to mark the impossible instance
argument which shows that no value need be returned. E.g.,
if tt then ff else magic : 2.
Instance arguments are not a perfect fit for proof search: they
were intended as a cheap alternative to type classes, hence the
requirement for exactly one candidate instance. For proofs we
might prefer to be less fussy about redundancy, but we shall manage
perfectly well for the purposes of this paper.
3.
Barking Up the Wrong Search Trees
David Turner [Turner(1987)] notes that whilst quicksort is often
cited as a program which defies structural recursion, it performs the
same sorting algorithm (although not with the same memory usage
pattern) as building a binary search tree and then flattening it. The
irony is completed by noting that the latter sorting algorithm is the
archetype of structural recursion in Rod Burstall’s development of
the concept [Burstall(1969)]. Binary search trees have empty leaves
and nodes labelled with elements which act like pivots in quicksort:
the left subtree stores elements which precede the pivot, the right
subtree elements which follow it. Surely this invariant is crying out
to be a dependent type! Let us search for a type for search trees.
We could, of course, define binary search trees as ordinary nodelabelled trees with parameter P giving the type of pivots:
data Tree : Set where
leaf : Tree; node : Tree → P → Tree → Tree
We might then define the invariant as a predicate IsBST : Tree →
Set, implement insertion in our usual way, and prove separately
that our program maintains the invariant. However, the joy of dependently typed programming is that refining the types of the data
themselves can often alleviate or obviate the burden of proof. Let
us try to bake the invariant in.
What should the type of a subtree tell us? If we want to check
the invariant at a given node, we shall need some information about
the subtrees which we might expect comes from their type. We
require that the elements left of the pivot precede it, so we could
require the whole set of those elements represented somehow, but
of course, for any order worthy of the name, it suffices to check only
the largest. Similarly, we shall need to know the smallest element
of the right subtree. It would seem that we need the type of a search
tree to tell us its extreme elements (or that it is empty).
data STRange : Set where
− : P → P → STRange
∅ : STRange;
infix 9 −
From checking the invariant to enforcing it. Assuming we can
test the order on P with some le : P → P → 2, we could write
a recursive function to check whether a Tree is a valid search tree
and compute its range if it has one. Of course, we must account for
the possibility of invalidity, so let us admit failure in the customary
manner.
data Maybe (X : Set) : Set where
yes : X → Maybe X ;
no : Maybe X
?i : ∀{X } → 2 → Maybe X → Maybe X
b ?i mx = if b then mx else no
infixr 4 ?i
The guarding operator ?i allows us to attach a Boolean test. We
may now validate the range of a Tree.
valid : Tree → Maybe STRange
valid leaf = yes ∅
valid (node l p r ) with valid l | valid r
... | yes ∅
| yes ∅
= yes (p − p)
... | yes ∅
| yes (c − d ) = le p c ?i yes (p − d )
... | yes (a − b) | yes ∅
= le b p ?i yes (a − p)
... | yes (a − b) | yes (c − d )
= le b p ?i le p c ?i yes (a − d )
|
= no
... |
As valid is a fold over the structure of Tree, we can follow my
colleagues Bob Atkey, Neil Ghani and Patricia Johann in computing the partial refinement [Atkey et al.(2012)Atkey, Johann, and Ghani]
of Tree which valid induces. We seek a type BST : STRange →
Set such that BST r ∼
= {t : Tree | valid t = yes r } and we find
it by refining the type of each constructor of Tree with the check
performed by the corresponding case of valid, assuming that the
subtrees yielded valid ranges. We can calculate the conditions to
check and the means to compute the output range if successful.
lOK : STRange → P → 2
lOK ∅
p = tt
lOK ( − u) p = le u p
rOK : P → STRange → 2
rOK p ∅
= tt
rOK p (l − ) = le p l
rOut : STRange → P → STRange → STRange
rOut ∅
p∅
= p −p
rOut ∅
p ( − u) = p − u
= l −p
rOut (l − ) p ∅
rOut (l − ) ( − u) = l − u
We thus obtain the following refinement from Tree to BST:
data BST : STRange → Set where
leaf : BST ∅
node : ∀{l r } → BST l → (p : P ) → BST r →
So (lOK l p) ⇒ So (rOK p r ) ⇒ BST (rOut l p r )
Attempting to implement insertion. Now that each binary search
tree tells us its type, can we implement insertion? Rod Burstall’s
implementation is as follows
insert : P → Tree → Tree
insert y leaf
= node leaf y leaf
insert y (node lt p rt) =
if le y p then node (insert y lt) p rt
else node lt p (insert y rt)
but we shall have to try a little harder to give a type to insert, as
we must somehow negotiate the ranges. If we are inserting a new
extremum, then the output range will be wider than the input range.
oRange : STRange → P → STRange
oRange ∅
y = y −y
oRange (l − u) y =
if le y l then y − u else if le u y then l − y else l − u
So, we have the right type for our data and for our program.
Surely the implementation will go like clockwork!
insert : ∀{r } y → BST r → BST (oRange r y)
insert y leaf
= node leaf y leaf
insert y (node lt p rt) =
if le y p then (node (insert y lt) p rt)
else (node lt p (insert y rt))
The leaf case checks easily, but alas for node! We have lt :
BST l and rt : BST r for some ranges l and r . The then
branch delivers a BST (rOut (oRange l y) p r ), but the
type required is BST (oRange (rOut l p r ) y), so we need
some theorem-proving to fix the types, let alone to discharge the
obligation So (lOK (oRange l y) p). We could plough on with
proof and, coughing, push this definition through, but tough work
ought to make us ponder if we might have thought askew.
We have defined a datatype which is logically correct but which
is pragmatically disastrous. Is it thus inevitable that all datatype
definitions which enforce the ordering invariant will be pragmatically disastrous? Or are there lessons we can learn about dependently typed programming that will help us to do better?
4.
In doing so, we eliminate all the ‘green slime’ from the indices of
the type. The leaf constructor now has many types, indicating all
its elements satisfy any requirements. We also gain BST ⊥ > as
the general type of binary search trees for P . Unfortunately, we
have been forced to make the pivot value p, the first argument to
pnode, as the type of the subtrees now depends on it. Luckily,
Agda now supports pattern synonyms, allowing linear macros to
abbreviate both patterns on the left and pattern-like expressions on
the right [Aitken and Reppy(1992)]. We may fix up the picture:
pattern node lp p pu = pnode p lp pu
Can we implement insert for this definition? We can certainly
give it a rather cleaner type. When we insert a new element into the
left subtree of a node, we must ensure that it precedes the pivot: that
is, we expect insertion to preserve the bounds of the subtree, and
we should already know that the new element falls within them.
insert : ∀{l u } y → BST l u →
>
So (le >
⊥ l (#y)) ⇒ So (le ⊥ (#y) u) ⇒ BST l u
insert y leaf
= node leaf y leaf
insert y (node lt p rt) =
if le y p then node (insert y lt) p rt
else node lt p ( insert y rt )
Why Measure When You Can Require?
Last section, we got the wrong answer because we asked the wrong
question: “What should the type of a subtree tell us?” somewhat
presupposes that information bubbles outward from subtrees to the
nodes which contain them. In Milner’s tradition, we are used to
synthesizing the type of a thing. Moreover, the very syntax of data
declarations treats the index delivered from each constructor as an
output. It seems natural to treat datatype indices as measures of
the data. That is all very well for the length of a vector, but when
the measurement is intricate, as when computing a search tree’s
extrema, programming becomes vexed by the need for theorems
about the measuring functions. The presence of ‘green slime’—
defined functions in the return types of constructors—is a danger
sign.
We can take an alternative view of types, not as synthesized
measurements of data, bubbled outward, but as checked requirements of data, pushed inward. To enforce the invariant, let us rather
ask “What should we tell the type of a subtree?”.
The elements of the left subtree must precede the pivot in the
order; those of the right must follow it. Correspondingly, our requirements on a subtree amount to an interval in which its elements
must fall. As any element can find a place somewhere in a search
tree, we shall need to consider unbounded intervals also. We can
extend any type with top and bottom elements as follows.
data >
⊥ (P : Set) : Set where
>
>
> : P>
⊥ ; # : P → P⊥ ; ⊥ : P⊥
and extend the order accordingly:
>
⊥ : ∀{P } →
le >
>
⊥
le >
(
#
x
)
(
#y)
⊥
le >
⊥
⊥
le >
⊥
>
(P → P → 2) → P>
⊥ → P⊥ → 2
= tt
= le x y
= tt
= ff
We can now index search trees by a pair of loose bounds, not
measuring the range of the contents exactly, but constraining it
sufficiently. At each node, we can require that the pivot falls in the
interval, then use the pivot to bound the subtrees.
data BST (l u : P>
⊥ ) : Set where
leaf
: BST l u
pnode : (p : P ) → BST l (#p) → BST (#p) u →
>
So (le >
⊥ l (#p)) ⇒ So (le ⊥ (#p) u) ⇒ BST l u
We have no need to repair type errors by theorem proving, and most
of our proof obligations follow directly from our assumptions. The
recursive call in the then branch requires a proof of So (le y p),
but that is just the evidence delivered by our evidence-transmitting
conditional. However, the else case snatches defeat from the jaws
of victory: the recursive call needs a proof of So (le p y), but all
we have is a proof of So (¬ (le y p)). For any given total ordering,
we should be able to fix this mismatch up by proving a theorem, but
this is still more work than I enjoy. The trouble is that we couched
our definition in terms of the truth of bits computed in a particular
way, rather than the ordering relation. Let us now tidy up this detail.
5.
One Way Or The Other
We can recast our definition in terms of relations—families of sets
Rel P indexed by pairs.
Rel : Set → Set1
Rel P = P × P → Set
giving us types which directly make statements about elements of
P , rather than about bits.
I must, of course, say how such pairs are defined: the habit of
dependently typed programmers is to obtain them as the degenerate
case of dependent pairs: let us have them.
record Σ (S : Set) (T : S → Set) : Set where
constructor ,
field π1 : S ; π2 : T π1
open Σ
× : Set → Set → Set
S ×T = ΣS λ → T
infixr 5 × ,
Now, suppose we have some ‘less or equal’ ordering L : Rel P .
Let us have natural numbers by way of example,
data N : Set where 0 : N; s : N → N
LN : Rel N
LN (x, y) = x ≤ y where
≤ : N → N → Set
0 ≤y = 1
sx ≤0 = 0
sx ≤sy = x ≤y
The information we shall need is exactly the totality of L: for
any given x and y, L must hold one way or the other. We can use
disjoint sum types for that purpose
data + (S T : Set) : Set where
/ : S → S + T;
. : T → S +T
infixr 4 +
OWOTO : ∀{P } (L : Rel P ) → Rel P
OWOTO L (x, y) = pL (x, y)q + pL (y, x )q
pattern le = / !
pattern ge = . !
I have used pattern synonyms to restore the impression that we
are just working with a Boolean type, but the ! serves to unpack
evidence when we test and to pack it when we inform. We shall
usually be able to keep silent about ordering evidence, even from
the point of its introduction. For N, let us have
owoto : ∀x y →
owoto 0
y
owoto (s x ) 0
owoto (s x ) (s y)
OWOTO LN (x, y)
= le
= ge
= owoto x y
Note that we speak only of the crucial bit of information. Moreover, we especially benefit from type-level computation in the
step case: OWOTO LN (s x , s y) is the very same type as
OWOTO LN (x, y).
Any ordering relation on elements lifts readily to bounds. Let us
take the opportunity to add propositional wrapping, to help us hide
ordering proofs.
>
q : ∀{P } → Rel P
⊥ p
L>
, >) = 1
⊥ (
L>
⊥ (#x, #y) = L (x, y)
L>
= 1
⊥ (⊥ , )
L>
, ) = 0
⊥ (
pLq xy = pL >
⊥ xyq
→ Rel
looks like its simply typed counterpart. You can check that for any
˙ T ] and so forth.
S and T , / : [S →
˙ S +
7.
It will be useful to consider sets indexed by bounds in the same
framework as relations on bounds: propositions-as-types means we
have been doing this from the start! Useful combinator on such
sets is the pivoted pair, S ∧˙ T , indicating that some pivot value
p exists, with S holding before p and T afterwards. A pattern
synonym arranges the order neatly.
>
>
∧˙ : ∀{P } → Rel P>
⊥ → Rel P⊥ → Rel P⊥
˙∧ {P } S T (l, u) = Σ P λ p → S (l, #p) × T (#p, u)
pattern
s p t = p, s, t
‘‘
infixr 5
‘‘
Immediately, we can define an interval as an element within
proven bounds.
•
: ∀{P } (L : Rel P ) → Rel P>
⊥
L = pLq ∧˙ pLq
pattern ◦ p = ! p !
‘ ‘
In habitual tidiness, a pattern synonym conceals the evidence.
Let us then parametrize over some
•
owoto : ∀x y → OWOTO L (x, y)
and reorganise our development.
>
data BST (lu : P>
⊥ × P⊥ ) : Set where
leaf
: BST lu
˙ BST) ∧˙ (pLq ×
˙ BST) →
pnode : ((pLq ×
˙ BST) lu
pattern node lt p rt = pnode (p, (!, lt), (!, rt))
P>
⊥
Reassuringly, the standard undergraduate error, arising from
thinking about doing not being, is now ill typed.
insert : [L• →
˙ BST →
˙ BST]
insert y◦ leaf
= node leaf y leaf
insert y◦ (node lt p rt) with owoto y p
... | le = (insert y◦ lt)
The type pLq (x , y) thus represents ordering evidence on bounds
with matching and construction by !, unaccompanied.
6.
Equipment for Relations and Other Families
Before we get back to work in earnest, let us build a few tools
for working with relations and other such indexed type families: a
relation is a family which happens to be indexed by a pair. We shall
have need of pointwise truth, falsity, conjunction, disjunction and
implication.
0˙ 1˙ : {I : Set} → I → Set
0˙ i = 0
1˙ i = 1
˙ ×
˙ →
+
˙ : {I : Set} →
(I → Set) → (I → Set) → I → Set
˙ T) i = S i + T i
(S +
˙ T) i = S i × T i
(S ×
(S →
˙ T) i = S i → T i
˙ ; infixr 4 ×
˙ ; infixr 2 →
˙
infixr 3 +
Pointwise implication will be useful for writing index-respecting
functions, e.g., bounds-preserving operations. It is useful to be able
to state that something holds at every index (i.e., ‘always works’).
[ ] : {I : Set} → (I → Set) → Set
[F ] = ∀{i } → F i
With this apparatus, we can quite often talk about indexed things
without mentioning the indices, resulting in code which almost
Working with Bounded Sets
... | ge = (insert y◦ rt)
However, once we remember to restore the unchanged parts of
the tree, we achieve victory, at last!
insert : [L• →
˙ BST →
˙ BST]
insert y◦ leaf
= node leaf y leaf
insert y◦ (node lt p rt) with owoto y p
... | le = node (insert y◦ lt) p rt
... | ge = node lt p (insert y◦ rt)
The evidence generated by testing owoto y p is just what is
needed to access the appropriate subtree. We have found a method
which seems to work! But do not write home yet.
8.
The Importance of Local Knowledge
Our current representation of an ordered tree with n elements
contains 2n pieces of ordering evidence, which is n − 1 too many.
We should need only n + 1 proofs, relating the lower bound to
the least element, then comparing neighbours all the way along to
the greatest element (one per element, so far) which must then fall
below the upper bound (so, one more). As things stand, the pivot
at the root is known to be greater than every element in the right
spine of its left subtree and less than every element in the left spine
of its right subtree. If the tree was built by iterated insertion, these
comparisons will surely have happened, but that does not mean we
should retain the information.
Suppose, for example, that we want to rotate a tree, perhaps to
keep it balanced, then we have a little local difficulty:
rotR : [BST →
˙ BST]
rotR (node (node lt m mt) p rt)
= node lt m (node mt p rt)
rotR t = t
Agda rejects the outer node of the rotated tree for lack of evidence.
I expand the pattern synonyms to show what is missing.
rotR : [BST →
˙ BST]
rotR (pnode
((! {{lp }}, pnode ((! {{lm }}, lt) m (! {{mp }}, mt)))
‘ ‘
p (! {{pu }}, rt))) = pnode ((! {{lm }}, lt) m
‘ ‘
‘ ‘
(! {{ ?0 }}, pnode ((! {{mp }}, mt) p (! {{pu }}, rt))))
‘ ‘
rotR t = t
We can discard the non-local ordering evidence lp : L >
⊥ (l, # p),
but now we need the non-local ?0 : L >
⊥ (#m, u) that we lack. Of
course, we can prove this goal from mp and pu if L is transitive,
but if we want to make less work, we should rather not demand
non-local ordering evidence in the first place.
Looking back at the type of node, note that the indices at which
we demand ordering are the same as the indices at which we
demand subtrees. If we strengthen the invariant on trees to ensure
that there is a sequence of ordering steps from the lower to the upper
bound, we could dispense with the sometimes non-local evidence
stored in nodes, at the cost of a new constraint for leaf.
>
data BST (lu : P>
⊥ × P⊥ ) : Set where
pleaf : (pLq →
˙ BST) lu
pnode : (BST ∧˙ BST →
˙ BST) lu
pattern leaf
= pleaf !
pattern node lt p rt = pnode (lt p rt)
‘ ‘
Indeed, a binary tree with n nodes will have n+1 leaves. An inorder traversal of a binary tree is a strict alternation, leaf-node-leaf. . . -node-leaf, making a leaf the ideal place to keep the evidence
that neighbouring nodes are in order! Insertion remains easy.
insert : [L• →
˙ BST →
˙ BST]
insert y◦ leaf = node leaf y leaf
insert y◦ (node lt p rt) with owoto y p
... | le = node (insert y◦ lt) p rt
... | ge = node lt p (insert y◦ rt)
Rotation becomes very easy, with no proofs to rearrange!
rotR : [BST →
˙ BST]
rotR (node (node lt m mt) p rt) = node lt m (node mt p rt)
rotR t = t
We have arrived at a neat way to keep a search tree in order,
storing pivot elements at nodes and proofs in leaves. Phew!
But it is only the end of the beginning. To complete our sorting
algorithm, we need to flatten binary search trees to ordered lists.
Are we due another long story about the discovery of a good
definition of the latter? Fortunately not! The key idea is that an
ordered list is just a particularly badly balanced binary search
tree, where every left subtree is a leaf. We can nail that down in
short order, just by inlining leaf’s data in the left subtree of node,
yielding a sensible cons.
>
data OList (lu : P>
⊥ × P⊥ ) : Set where
nil : (pLq →
˙ OList) lu
cons : (pLq ∧˙ OList →
˙ OList) lu
These are exactly the ordered lists Sam Lindley and I defined in
Haskell [Lindley and McBride(2013)], but now we can see where
the definition comes from.
By figuring out how to build ordered binary search trees, we
have actually discovered how to build quite a variety of in-order
data structures. We simply need to show how the data are built from
particular patterns of BST components. So, rather than flattening
binary search trees, let us pursue a generic account of in-order
datatypes, then flatten them all.
9.
Jansson and Jeuring’s PolyP Universe
If we want to see how to make the treatment of ordered container
structures systematic, we shall need some datatype-generic account of recursive types with places for elements. A compelling
starting point is the ‘PolyP’ system of Patrik Jansson and Johan Jeuring [Jansson and Jeuring(1997)], which we can bottle as
a universe—a system of codes for types—in Agda, as follows:
data JJ : Set where
‘R ‘P ‘1 : JJ
‘+ ‘× : JJ → JJ → JJ
infixr 4 ‘+
infixr 5 ‘×
The ‘R stands for ‘recursive substructure’ and the ‘P stands for
‘parameter’—the type of elements stored in the container. Given
meanings for these, we interpret a code in JJ as a set. two codes.
J KJJ : JJ → Set → Set → Set
J ‘R KJJ
RP = R
J ‘P KJJ
RP = P
J ‘1 KJJ
RP = 1
J S ‘+ T KJJ R P = J S KJJ R P + J T KJJ R P
J S ‘× T KJJ R P = J S KJJ R P × J T KJJ R P
When we ‘tie the knot’ in µJJ F P , we replace interpret F ’s ‘Ps
by some actual P and its ‘Rs by µJJ F P .
data µJJ (F : JJ) (P : Set) : Set where
hi : J F KJJ (µJJ F P ) P → µJJ F P
Being finitary and first-order, all of the containers encoded by
JJ are traversable in the sense defined by Ross Paterson and myself
[McBride and Paterson(2008)].
record Applicative (H : Set → Set) : Set1 where
field
pure : ∀{X } → X → H X
ap : ∀{S T } → H (S → T ) → H S → H T
open Applicative
traverse : ∀{H F A B } → Applicative H →
(A → H B ) → µJJ F A → H (µJJ F B )
traverse {H } {F } {A} {B } AH h t = go ‘R t where
pu = pure AH ; ~ = ap AH
go : ∀G →
J G KJJ (µJJ F A) A → H (J G KJJ (µJJ F B ) B )
go ‘R
h t i = pu hi ~ go F t
go ‘P
a
= ha
go ‘1
hi
= pu hi
go (S ‘+ T ) (/ s) = pu / ~ go S s
go (S ‘+ T ) (. t) = pu . ~ go T t
go (S ‘× T ) (s, t) = (pu , ~ go S s) ~ go T t
We can specialise traverse to standard functorial map.
idApp : Applicative (λ X → X )
idApp = record {pure = id; ap = id}
map : ∀{F A B } →
(A → B ) → µJJ F A → µJJ F B
map = traverse idApp
We can equally well specialise traverse to a monoidal crush.
is to introduce a helper function, go, whose type separates G,
the structure of the top node, from F the structure of recursive
subnodes, allowing us to take the top node apart: we kick off with
G = F.
tree : ∀{P F } → µSO F P → µSO ‘Tree P
tree {P } {F } h f i = go F f where
go : ∀G → J J G KSO KJJ (µSO F P ) P → µSO ‘Tree P
go ‘R
f
= tree f
go ‘1
hi
= h / hi i
go (S ‘+ T ) (/ s) = go S s
go (S ‘+ T ) (. t)
= go T t
go (S ‘∧ T ) (s, p, t) = h . (go S s, p, go T t) i
record Monoid (X : Set) : Set where
field neutral : X ; combine : X → X → X
monApp : Applicative (λ → X )
monApp = record {pure = λ → neutral; ap = combine}
crush : ∀{P F } → (P → X ) → µJJ F P → X
crush = traverse {B = 0} monApp
open Monoid
Endofunctions on a given set form a monoid with respect to
composition, which allows us a generic foldr-style operation.
compMon : ∀{X } → Monoid (X → X )
compMon = record
{neutral = id; combine = λ f g → f ◦ g }
foldr : ∀{F A B } →
(A → B → B ) → B → µJJ F A → B
foldr f b t = crush compMon f t b
All tree does is strip out the /s and .s corresponding to the
structural choices offered by the input type and instead label the
void leaves / and the pivoted nodes .. Note well that a singleton
tree has void leaves as its left and right substructures, and hence
that the inorder traversal is a strict alternation of leaves and pivots,
beginning with the leaf at the end of the left spine and ending with
the leaf at the end of the right spine. As our tree function preserves
the leaf/pivot structure of its input, we learn that every datatype we
can define in SO stores such an alternation of leaves and pivots.
We can use foldr to build up B s from any structure containing As,
given a way to ‘insert’ an A into a B , and an ‘empty’ B to start
with. Let us check that our generic machinery is fit for purpose.
10.
2
The Simple Orderable Subuniverse of JJ
The quicksort algorithm divides a sorting problem in two by partitioning about a selected pivot element the remaining data. Rendered as the process of building then flattening a binary search tree
[Burstall(1969)], the pivot element clearly marks the upper bound
of the lower subtree and the lower bound of the upper subtree, giving exactly the information required to guide insertion.
We can require the presence of pivots between substructures by
combining the parameter ‘P and pairing ‘× constructs of the PolyP
universe into a single pivoting construct, ‘∧, with two substructures
and a pivot in between. We thus acquire the simple orderable
universe, SO, a subset of JJ picked out as the image of a function,
J KSO . Now, P stands also for pivot!
data SO : Set where
‘R ‘1
: SO
‘+ ‘∧ : SO → SO → SO
infixr 5 ‘∧
J KSO : SO → JJ
J ‘R KSO
= ‘R
J ‘1 KSO
= ‘1
J S ‘+ T KSO = J S KSO ‘+ J T KSO
J S ‘∧ T KSO = J S KSO ‘× ‘P ‘× J T KSO
µSO : SO → Set → Set
µSO F P = µJJ J F KSO P
Let us give SO codes for structures we often order and bound:
‘List ‘Tree ‘Interval : SO
‘List
= ‘1 ‘+ (‘1 ‘∧ ‘R)
‘Tree
= ‘1 ‘+ (‘R ‘∧ ‘R)
‘Interval = ‘1 ‘∧ ‘1
Every data structure described by SO is a regulated variety of
node-labelled binary trees. Let us check that we can turn anything
into a tree, preserving the substructure relationship. The method1
1 If
4
you try constructing the division operator as a primitive recursive function, this method will teach itself to you.
1
8
3
6
5
9
1
2
3
4
5
7
6
7
8
9
We are now in a position to roll out the “loose bounds” method
to the whole of the SO universe. We need to ensure that each pivot
is in order with its neighbours and with the outer bounds, and the
alternating leaf/pivot structure gives us just what we need: let us
store the ordering evidence at the leaves!
J K≤
SO : SO →
J ‘R K≤
R
SO
J ‘1 K≤
R
SO
J S ‘+ T K≤
SO R
J S ‘∧ T K≤
SO R
>
∀{P } → Rel P>
⊥ → Rel P → Rel P⊥
L = R
L = pLq
≤
˙
L = J S K≤
SO R L + J T KSO R L
≤
≤
L = J S KSO R L ∧˙ J T KSO R L
data µ≤
SO (F : SO) {P : Set} (L : Rel P )
>
(lu : P>
⊥ × P⊥ ) : Set where
≤
≤
hi : J F KSO (µSO F L) L lu → µ≤
SO F L lu
We have shifted from sets to relations, in that our types are indexed
by lower and upper bounds. The leaves demand evidence that the
bounds are in order, whilst the nodes require the pivot first, then use
it to bound the substructures appropriately.
Meanwhile, the need in nodes to bound the left substructure’s
type with the pivot value disrupts the left-to-right spatial ordering
of the data, but we can apply a little cosmetic treatment, thanks to
the availability of pattern synonyms.
With these two devices available, let us check that we can still
turn any ordered data into an ordered tree, writing L ∆ l u for
µ≤
SO ‘Tree L l u, and redefining intervals accordingly.
ƥ
: ∀{P } → Rel P → Rel P>
⊥
L∆ = µ≤
L
SO ‘Tree
pattern leaf
= h/ !i
pattern node lp p pu = h . (lp p pu) i
‘ ‘
L• = µ≤
SO ‘Interval L
pattern ◦ p = h (p, !, !) i
tree : ∀{P F } {L : Rel P } → [µ≤
˙ L∆ ]
SO F L →
tree {P } {F } {L} h f i = go F f where
≤
go : ∀G → [J G K≤
˙ L∆ ]
SO (µSO F L) L →
go ‘R
f
= tree f
go ‘1
!
= leaf
go (S ‘+ T ) (/ s)
= go S s
go (S ‘+ T ) (. t)
= go T t
go (S ‘∧ T ) (s p t) = node (go S s) p (go T t)
‘ ‘
We have acquired a collection of orderable datatypes which
all amount to specific patterns of node-labelled binary trees: an
interval is a singleton node; a list is a right spine. All share the
treelike structure which ensures that pivots alternate with leaves
bearing the evidence the pivots are correctly placed with respect to
their immediate neighbours.
Let us check that we are where we were, so to speak. Hence we
can rebuild our binary search tree insertion for an element in the
corresponding interval:
insert : [L• →
˙ L∆ →
˙ L∆ ]
◦
insert y leaf
= node leaf y leaf
insert y◦ (node lt p rt) with owoto y p
... | le = node (insert y◦ lt) p rt
... | ge = node lt p (insert y◦ rt)
The constraints on the inserted element are readily expressed via
our ‘Interval type, but at no point need we ever name the ordering
evidence involved. The owoto test brings just enough new evidence
into scope that all proof obligations on the right-hand side can be
discharged by search of assumptions. We can now make a search
tree from any input container.
makeTree : ∀{F } → µJJ F P → L∆ (⊥, >)
makeTree = foldr (λ p → insert h ! p ! i) h / ! i
‘ ‘
11.
Digression: Merging Monoidally
Let us name our family of ordered lists L+ , as the leaves form a
nonempty chain of pLq ordering evidence.
: ∀{P } → Rel P → Rel P>
⊥
L = µ≤
SO ‘List L
pattern []
= h/ !i
pattern :: x xs = h . (x, !, xs) i
infixr 6 ::
+
+
ys
[]
merge : [L+ →
˙ L+ →
˙ L+ ]
merge
[]
= id
merge {l, u } (x :: xs) = go where
+
go : ∀{l } {{ : L >
˙ L+ ) (l, u)
⊥ (l, #x )}} → (L →
go []
= x :: xs
go (y :: ys) with owoto x y
... | le = x :: merge xs (y :: ys)
... | ge = y :: go ys
The helper function, go inserts x at its rightful place in the second
list, then resumes merging with xs.
Merging equips ordered lists with monoidal structure.
+
olMon : ∀{lu } → L >
⊥ lu ⇒ Monoid (L lu)
olMon = record {neutral = []; combine = merge}
An immediate consequence is that we gain a family of sorting
algorithms which amount to depth-first merging of a given intermediate data structure, making a singleton from each pivot.
mergeJJ : ∀{F } → µJJ F P → L+ (⊥, >)
mergeJJ = crush olMon λ p → p :: []
The instance of mergeJJ for lists is exactly insertion sort: at each
cons, the singleton list of the head is merged with the sorted tail. To
obtain an efficient mergeSort, we should arrange the inputs as a
leaf-labelled binary tree.
‘qLTree : JJ
‘qLTree = (‘1 ‘+ ‘P) ‘+ ‘R ‘× ‘R
pattern none
= h / (/ hi) i
pattern one p = h / (. p) i
pattern fork l r = h . (l, r ) i
We can add each successive elements to the tree with a twisting
insertion, placing the new element at the bottom of the left spine,
but swapping the subtrees at each layer along the way to ensure fair
distribution.
twistIn :
twistIn p
twistIn p
twistIn p
The next section addresses the issue of how to flatten ordered
structures to ordered lists, but let us first consider how to merge
them. Merging sorts differ from flattening sorts in that order is
introduced when ‘conquering’ rather than ‘dividing’.
We can be sure that whenever two ordered lists share lower
and upper bounds, they can be merged within the same bounds.
Again, let us assume a type P of pivots, with owoto witnessing the
totality of order L. The familiar definition of merge typechecks but
falls just outside the class of lexicographic recursions accepted by
Agda’s termination checker. I have dug out the concealed evidence
which causes the trouble.
merge : [L+ →
˙ L+ →
˙ L+ ]
merge []
merge xs
merge h . (! {{ }}) x xs i (y :: ys) with owoto x y
‘ ‘
... | le = x :: merge xs (y //ys)
... | ge = y :: merge h . ( (! {{ }}) x xs) i ys
‘ ‘
In one step case, the first list gets smaller, but in the other, where
we decrease the second list, the first does not remain the same: it
contains fresh evidence that x is above the tighter lower bound, y.
Separating the recursion on the second list is sufficient to show that
both recursions are structural.
= ys
= xs
P → µJJ ‘qLTree P → µJJ ‘qLTree P
none
= one p
(one q) = fork (one p) (one q)
(fork l r ) = fork (twistIn p r ) l
If we notice that twistIn maps elements to endofunctions on
trees, we can build up trees by a monoidal crush, obtaining an
efficient generic sort for any container in the JJ universe.
mergeSort : ∀{F } → µJJ F P → L+ (⊥, >)
mergeSort = mergeJJ ◦ foldr twistIn none
12.
Flattening With Concatenation
Several sorting algorithms amount to building an ordered intermediate structure, then flattening it to an ordered list. As all of our
orderable structures amount to trees, it suffices to flatten trees to
lists. Let us take the usual na¨ıve approach as our starting point. In
Haskell, we might write
flatten Leaf
= []
flatten (Node l p r) = flatten l ++ p : flatten r
so let us try to do the same in Agda with ordered lists. We shall
need concatenation, so let us try to join lists with a shared bound p
in the middle.
infixr 8 ++
++ : ∀{P } {L : Rel P } {l p u } →
L+ (l, p) → L+ (p, u) → L+ (l, u)
[]
++ ys = ys
(x :: xs) ++ ys = x :: xs ++ ys
The ‘cons’ case goes without a hitch, but there is trouble at ‘nil’.
>
We have ys : µ≤
SO ‘List L p u and we know L ⊥ l p, but we need
to return a µ≤
‘List
L
l
u.
SO
++
4
5
6
7
8
9
“The trouble is easy to fix,” one might confidently assert, whilst
secretly thinking, “What a nuisance!”. We can readily write a helper
function which unpacks ys, and whether it is nil or cons, extends its
leftmost order evidence by transitivity. And this really is a nuisance,
because, thus far, we have not required transitivity to keep our
code well typed: all order evidence has stood between neighbouring
elements. Here, we have two pieces of ordering evidence which we
must join, because we have nothing to put in between them. Then,
the penny drops. Looking back at the code for flatten, observe that
p is the pivot and the whole plan is to put it between the lists. You
can’t always get what you want, but you can get what you need.
˙ L+ ]
sandwich : ∀{P } {L : Rel P } → [(L+ ∧˙ L+ ) →
sandwich ([]
p ys) = p :: ys
‘ ‘
sandwich (x :: xs p ys) = x :: sandwich (xs p ys)
‘ ‘
‘ ‘
We are now ready to flatten trees, thence any ordered structure:
flatten : ∀{P } {L : Rel P } → [L∆ →
˙ L+ ]
flatten leaf
= []
flatten (node l p r ) = sandwich (flatten l p flatten r )
‘ ‘
≤
flatten≤
˙ L+ ]
SO : ∀{P } {L : Rel P } {F } → [µSO F L →
≤
flattenSO = flatten ◦ tree
For a little extra speed we might fuse that composition, but
it seems frivolous to do so as then benefit is outweighed by the
quadratic penalty of left-nested concatenation. The standard remedy applies: we can introduce an accumulator [Wadler(1987)], but
our experience with ++ should alert us to the possibility that it may
require some thought.
13.
Faster Flattening, Generically
We may define flatten generically, and introduce an accumulator
yielding a combined flatten-and-append which works right-to-left,
growing the result with successive conses. But what should be the
bounds of the accumulator? If we have not learned our lesson, we
might be tempted by
flapp : ∀{F P } {L : Rel P } {l p u } →
+
+
µ≤
SO F L (l, p) → L (p, u) → L (l, u)
but again we face the question of what to do when we reach a
leaf. We should not need transitivity to rearrange a tree of ordered
neighbours into a sequence. We can adopt the previous remedy
of inserting the element p in the middle, but we shall then need
to think about where p will come from in the first instance, for
example when flattening an empty structure.
flapp : ∀{F P } {L : Rel P } G →
≤
˙ + ˙ L+ ]
[J G K≤
SO (µSO F L) L ∧ L →
flapp {F } ‘R (h t i
p ys) = flapp F (t p ys)
‘ ‘
‘ ‘
flapp ‘1
(!
p ys) = p :: ys
‘ ‘
flapp (S ‘+ T ) (/ s
p ys) = flapp S (s p ys)
‘ ‘
‘ ‘
flapp (S ‘+ T ) (. t
p ys) = flapp T (t p ys)
‘ ‘
‘ ‘
0
flapp (S ‘∧ T ) ((s p t) p ys)
0‘ ‘ ‘ ‘
= flapp S (s p flapp T (t p ys))
‘ ‘
‘ ‘
To finish the job, we need to work our way down the right spine of
the input in search of its rightmost element, which initialises p.
flatten : ∀{F P } {L : Rel P } → [µ≤
˙ L+ ]
SO F L →
flatten {F } {P } {L} {l, u } h t i = go F t where
≤
+
go : ∀{l } G → J G K≤
SO (µSO F L) L (l, u) → L (l, u)
go ‘R
t
= flatten t
go ‘1
!
= []
go (S ‘+ T ) (/ s)
= go S s
go (S ‘+ T ) (. t)
= go T t
go (S ‘∧ T ) (s p t) = flapp S (s p go T t)
‘ ‘
‘ ‘
This is effective, but it is more complicated than I should like.
It is basically the same function twice, in two different modes, depending on what is to be affixed after the rightmost order evidence
in the structure being flattened: either a pivot-and-tail in the case
of flapp, or nothing in the case of flatten. The problem is one of
parity: the thing we must affix to one odd-length leaf-node-leaf
alternation to get another is an even-length node-leaf alternation.
Correspondingly, it is hard to express the type of the accumulator
cleanly. Once again, I begin to suspect that this is a difficult thing
to do because it is the wrong thing to do. How can we reframe the
problem, so that we work only with odd-length leaf-delimited data?
14.
A Replacement for Concatenation
My mathematical mentor, Tom K¨orner, is fond of remarking “A
mathematician is someone who knows that 0 is 0 + 0”. It is often
difficult to recognize the structure you need when the problem in
front of you is a degenerate case of it. If we think again about
concatenation, we might realise that it does not amount to affixing
one list to another, but rather replacing the ‘nil’ of the first list with
the whole of the second. We might then notice that the monoidal
structure of lists is in fact degenerate monadic structure.
Any syntax has a monadic structure, where ‘return’ embeds
variables as terms and ‘bind’ is substitution. Quite apart from their
‘prioritised choice’ monadic structure, lists are the terms of a degenerate syntax with one variable (called ‘nil’) and only unary operators (‘cons’ with a choice of element). Correspondingly, they
have this substitution structure: substituting nil gives concatenation, and the monad laws are the monoid laws.
Given this clue, let us consider concatenation and flattening in
terms of replacing the rightmost leaf by a list, rather than affixing
more data to it. We replace the list to append with a function which
maps the contents of the rightmost leaf—some order evidence—
to its replacement. The type looks more like that of ‘bind’ than
‘append’, because in some sense it is!
infixr 8 ++
RepL : ∀{P } → Rel P → Rel P>
⊥
+
RepL L (n, u) = ∀{m } → L >
⊥ (m, n) ⇒ L (m, u)
++ : ∀{P } {L : Rel P } {l n u } →
L+ (l, n) → RepL L (n, u) → L+ (l, u)
[]
++ ys = ys
(x :: xs) ++ ys = x :: xs ++ ys
Careful use of instance arguments leaves all the manipulation of
evidence to the machine. In the ‘nil’ case, ys is silently instantiated
with exactly the evidence exposed in the ‘nil’ pattern on the left.
Let us now deploy the same technique for flatten.
flapp : ∀{P } {L : Rel P } {F } {l n u } →
+
µ≤
SO F L (l, n) → RepL L (n, u) → L (l, u)
flapp {P } {L} {F } {u = u } t ys = go ‘R t ys where
≤
go : ∀{l n } G → J G K≤
SO (µSO F L) L (l, n) →
+
RepL L (n, u) → L (l, u)
go ‘R
ht i
ys = go F t ys
go ‘1
!
ys = ys
go (S ‘+ T ) (/ s) ys = go S s ys
go (S ‘+ T ) (. t) ys = go T t ys
go (S ‘∧ T ) (s p t) ys = go S s (p :: go T t ys)
‘ ‘
flatten : ∀{P } {L : Rel P } {F } → [µ≤
˙ L+ ]
SO F L →
flatten t = flapp t []
15.
An Indexed Universe of Orderable Data
Ordering is not the only invariant we might want to enforce on orderable data structures. We might have other properties in mind,
such as size, or balancing invariants. It is straightforward to extend
our simple universe to allow general indexing as well as orderability. We can extend our simple orderable universe SO to an indexed
orderable universe IO, just by marking each recursive position with
an index, then computing the code for each node as a function of
its index. We may add a ‘0 code to rule out some cases as illegal.
data IO (I : Set) : Set where
‘R
: I → IO I
‘0 ‘1
: IO I
‘+ ‘∧ : IO I → IO I → IO I
J K≤
IO :
∀{I P } → IO I →
>
(I → Rel P>
⊥ ) → Rel P → Rel P⊥
≤
J ‘R i KIO
RL = Ri
J ‘0 K≤
R
L = λ → 0
IO
J ‘1 K≤
R
L = pLq
IO
≤
˙
J S ‘+ T K≤
R
L
= J S K≤
IO
IO R L + J T KIO R L
≤
≤
≤
J S ‘∧ T KIO R L = J S KIO R L ∧˙ J T KIO R L
data µ≤
IO {I P : Set} (F : I → IO I ) (L : Rel P )
>
(i : I ) (lu : P>
⊥ × P⊥ ) : Set where
≤
≤
hi : J F i KIO (µIO F L) L lu → µ≤
IO F L i lu
We recover all our existing data structures by trivial indexing.
‘List ‘Tree ‘Interval : 1 → IO 1
= ‘1 ‘+ (‘1 ‘∧ ‘R hi)
‘List
‘Tree
= ‘1 ‘+ (‘R hi ‘∧ ‘R hi)
‘Interval = ‘1 ‘∧ ‘1
We also lift our existing type-forming abbreviations:
+ƥ
+
L
L∆
L•
: ∀{P } → Rel P → Rel P>
⊥
= µ≤
‘List
L
hi
IO
= µ≤
L hi
IO ‘Tree
= µ≤
‘Interval
L hi
IO
However, we may also make profitable use of indexing: here are
ordered vectors.
‘Vec : N → IO N
‘Vec 0
= ‘1
‘Vec (s n) = ‘1 ‘∧ ‘R n
Note that we need no choice of constructor or storage of length
information: the index determines the shape. If we want, say, evenlength tuples, we can use ‘0 to rule out the odd cases.
‘Even : N →
‘Even 0
‘Even (s 0)
‘Even (s (s n))
IO N
= ‘1
= ‘0
= ‘1 ‘∧ ‘1 ‘∧ ‘R n
We could achieve a still more flexible notion of data structure
by allowing a general Σ-type rather than our binary ‘+, but we
have what we need for finitary data structures with computable
conditions on indices.
The tree operation carries over unproblematically, with more
indexed input but plain output.
tree : ∀{I P F } {L : Rel P } {i : I } →
[µ≤
˙ L∆ ]
IO F L i →
Similarly, flatten works (efficiently) just as before.
flatten : ∀{I P F } {L : Rel P } {i : I } →
[µ≤
˙ L+ ]
IO F L i →
We now have a universe of indexed orderable data structures
with efficient flattening. Let us put it to work.
16.
Balanced 2-3 Trees
To ensure a logarithmic access time for search trees, we can keep
them balanced. Maintaining balance as close to perfect as possible
is rather fiddly, but we can gain enough balance by allowing a little
redundancy. A standard way to achieve this is to insist on uniform
height, but allow internal nodes to have either one pivot and two
subtrees, or two pivots and three subtrees. We may readily encode
these 2-3 trees and give pattern synonyms for the three kinds of
structure. This approach is much like that of red-black (effectively,
2-3-4) trees, for which typesafe balancing has a tradition going
back to Hongwei Xi and Stefan Kahrs [Xi(1999), Kahrs(2001)].
‘Tree23 : N → IO N
‘Tree23 0
= ‘1
‘Tree23 (s h) = ‘R h ‘∧ (‘R h ‘+ (‘R h ‘∧ ‘R h))
: ∀{P } (L : Rel P ) → N → Rel P>
⊥
L = µ≤
IO ‘Tree23 L
pattern no0
= h!i
pattern no2 lt p rt
= h p, lt, / rt i
pattern no3 lt p mt q rt = h p, lt, . (q, mt, rt) i
23
23
When we map a 2-3 tree of height n back to binary trees, we
get a tree whose left spine has length n and whose right spine has
a length between n and 2n.
Insertion is quite similar to binary search tree insertion, except
that it can have the impact of increasing height. The worst that can
happen is that the resulting tree is too tall but has just one pivot at
the root. Indeed, we need this extra wiggle room immediately for
the base case!
ins23 : ∀h {lu } → L• lu → L23 h lu →
L23 h lu +
Σ P λ p → L23 h (π1 lu, #p) × L23 h (#p, π2 lu)
ins23 0 y◦ no0 = . (h ! i y h ! i)
‘ ‘
In the step case, we must find our way to the appropriate subtree by
suitable use of comparison.
ins23 (s h) y◦ h lt p rest i
‘ ‘
ins23 (s h) y◦ h lt p rest i
‘ ‘
ins23 (s h) y◦ (no2 lt p rt)
with owoto y p
| le = ?0
| ge = ?1
ins23 (s h) y◦ (no3 lt p mt q rt) | ge with owoto y q
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | le = ?2
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | ge = ?3
Our ?0 covers the case where the new element belongs in the left
subtree of either a 2- or 3-node; ?1 handles the right subtree of a
2-node; ?2 and ?3 handle middle and right subtrees of a 3-node
after a further comparison. Note that we inspect rest only after we
have checked the result of the first comparison, making real use of
the way the with construct brings more data to the case analysis but
keeps the existing patterns open to further refinement, a need foreseen by the construct’s designers [McBride and McKinna(2004)].
Once we have identified the appropriate subtree, we can make
the recursive call. If we are lucky, the result will plug straight back
into the same hole. Here is the case for the left subtree.
ins23 (s h) y◦ h lt p rest i | le
‘ ‘
with ins23 h y◦ lt
◦
ins23 (s h) y h lt p rest i | le
‘ ‘
| / lt 0 = / h lt 0 p rest i
‘ ‘
However, if we are unlucky, the result of the recursive call is too
big. If the top node was a 2-node, we can accommodate the extra
data by returning a 3-node. Otherwise, we must rebalance and pass
the ‘too big’ problem upward. Again, we gain from delaying the
inspection of rest until we are sure reconfiguration will be needed.
ins23 (s h) y◦ (no2 lt
| . (llt r lrt) =
‘ ‘
ins23 (s h) y◦ (no3 lt
| . (llt r lrt) =
‘ ‘
p rt)
/ (no3 llt r
p mt q rt)
. (no2 llt r
| le
lrt p rt)
| le
lrt p no2 mt q rt)
‘ ‘
For the ?1 problems, the top 2-node can always accept the result
of the recursive call somehow, and the choice offered by the return
type conveniently matches the node-arity choice, right of the pivot.
For completeness, I give the middle ( ?2 ) and right ( ?3 ) cases
for 3-nodes, but it works just as on the left.
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | le
with ins23 h y◦ mt
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | le
| / mt 0
= / (no3 lt p mt 0 q rt)
◦
ins23 (s h) y (no3 lt p mt q rt) | ge | le
| . (mlt r mrt) = . (no2 lt p mlt r no2 mrt q rt)
‘ ‘
‘ ‘
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | ge
◦
with ins23 h y rt
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | ge
| / rt 0
= / (no3 lt p mt q rt 0 )
ins23 (s h) y◦ (no3 lt p mt q rt) | ge | ge
| . (rlt r rrt) = . (no2 lt p mt q no2 rlt r rrt)
‘ ‘
‘ ‘
To complete the efficient sorting algorithm based on 2-3 trees,
we can use a Σ-type to hide the height data, giving us a type which
admits iterative construction.
Tree23 = Σ N λ h → L23 h (⊥, >)
insert : P → Tree23 → Tree23
insert p (h, t) with ins23 h p◦ t
... | / t 0
= h ,t0
... | . (lt r rt) = s h, no2 lt r rt
‘ ‘
sort : ∀{F } → µJJ F P → L+ (⊥, >)
sort = flatten ◦ π2 ◦ foldr insert (0, no0 )
17.
Deletion from 2-3 Trees
Might is right: the omission of deletion from treatments of balanced
search trees is always a little unfortunate. Deletion is a significant
additional challenge because we can lose a key from the middle of
the tree, not just from the fringe of nodes whose children are leaves.
Insertion acts always to extend the fringe, so the problem is only to
bubble an anomaly up from the fringe to the root. Fortunately, just
as nodes and leaves alternate in the traversal of a tree, so do middle
nodes and fringe nodes: whenever we need to delete a middle node,
it always has a neighbour at the fringe which we can move into the
gap, leaving us once more with the task of bubbling a problem up
from the fringe.
Our situation is further complicated by the need to restore the
neighbourhood ordering invariant when one key is removed. At
last, we shall need our ordering to be transitive. We shall also need
a decidable equality on keys.
data ≡ {X : Set} (x : X ) : X → Set where
hi : x ≡ x
infix 6 ≡
trans : ∀{x } y {z } → L (x, y) ⇒ L (y, z ) ⇒ pL (x, z )q
decEq : (x y : P ) → x ≡ y + (x ≡ y → 0)
Correspondingly, a small amount of theorem proving is indicated, ironically, to show that it is sound to throw information about
local ordering away.
Transitivity for bounds. Transitivity we may readily lift to
bounds with a key in the middle:
pattern via p = p, !, !
trans>
⊥
trans>
⊥
trans>
⊥
trans>
⊥
trans>
⊥
trans>
⊥
trans>
⊥
: [(pLq ∧˙ pLq) →
˙ pLq]
= !
{ , >}
{⊥ , ⊥}
= !
{⊥ , #u }
= !
{> , } (via ) = magic
{ #l, #u } (via p) = trans p ∴ !
{ #l, ⊥} (via ) = magic
What is the type of deletion? When we remove an element from
a 2-3 tree of height n, the tree will often stay the same height, but
there will be situations in which it must get shorter, becoming a
3-node or a leaf, as appropriate.
Del23 Short23 :
Del23 h lu
Short23 0 lu
Short23 (s h) lu
N → Rel P>
⊥
= Short23 h lu + L23 h lu
= 0
= L23 h lu
The task of deletion has three phases: finding the key to delete;
moving the problem to the fringe; plugging a short tree into a tall
hole. The first of these will be done by our main function,
del23 : ∀{h } → [L• →
˙ L23 h →
˙ Del23 h ]
and the second by extracting the extreme right key from a nonempty
left subtree,
extr : ∀{h } → [L23 (s h) →
˙ (Del23 (s h) ∧˙ L >
⊥ )]
recovering the (possily short) remainder of the tree and the evidence that the key is below the upper bound (which will be the
deleted key). Both of these operations will need to reconstruct trees
with one short subtree, so let us build ‘smart constructors’ for just
that purpose, then return to the main problem.
Rebalancing reconstructors. If we try to reconstruct a 2-node
with a possibly-short subtree, we might be lucky enough to deliver
a 2-node, or we might come up short. We certainly will not deliver
a 3-node of full height and it helps to reflect that in the type.
Shortness can be balanced out if we are adjacent to a 3-node, but if
we have only a 2-node, we must give a short answer.
Re2 : N → Rel P>
⊥
˙ (L23 h ∧˙ L23 h)
Re2 h = Short23 (s h) +
23
d2t : ∀{h } → [(Del h ∧˙ L23 h) →
˙ Re2 h ]
d2t {h } (. lp p pu)
= . (lp p pu)
‘ ‘
‘ ‘
d2t {0} (/ () p pu)
‘ ‘
d2t {s h } (/ lp p no2 pq q qu) = / (no3 lp p pq q qu)
‘ ‘
d2t {s h } (/ lp p no3 pq q qr r ru)
‘ ‘
= . (no2 lp p pq q no2 qr r ru)
‘ ‘
t2d : ∀{h } → [(L23 h ∧˙ Del23 h) →
˙ Re2 h ]
t2d {h } (lp p . pu)
= . (lp p pu)
‘ ‘
‘ ‘
t2d {0} (lp p / ())
‘ ‘
t2d {s h } (no2 ln n np p / pu) = / (no3 ln n np p pu)
‘ ‘
t2d {s h } (no3 lm m mn n np p / pu)
‘ ‘
= . (no2 lm m mn n no2 np p pu)
‘ ‘
rd : ∀{h } → [Re2 h →
˙ Del23 (s h)]
rd (/ s)
= (/ s)
rd (. (lp p pu)) = . (no2 lp p pu)
‘ ‘
The adaptor rd allows us to throw away the knowledge that the
full height reconstruction must be a 2-node if we do not need it,
but the extra detail allows us to use 2-node reconstructors in the
course of 3-node reconstruction. To reconstruct a 3-node with one
possibly-short subtree, rebuild a 2-node containing the suspect, and
then restore the extra subtree. We thus need to implement the latter.
r3t : ∀{h } → [(Re2 h ∧˙ L23 h) →
˙ Del23 (s h)]
r3t (. (lm m mp) p pu) = . (no3 lm m mp p pu)
‘ ‘
‘ ‘
r3t (/ lp p pu)
= . (no2 lp p pu)
‘ ‘
t3r : ∀{h } → [(L23 h ∧˙ Re2 h) →
˙ Del23 (s h)]
t3r (lp p . (pq q qu))
= . (no3 lp p pq q qu)
‘ ‘
‘ ‘
t3r (lp p / pu)
= . (no2 lp p pu)
‘ ‘
Cutting out the extreme right. We may now implement extr,
grabbing the rightmost key from a tree. I use
a
pattern 6 lr r = r, lr, !
to keep the extracted element on the right and hide the ordering
proofs.
extr : ∀{h } → [L23 (s h) →
˙ (Del23 (sah) ∧˙ pLq)]
extr {0} (no2 lr r no0 )
= / lr 6 r
a
extr {0} (no3 lp p pr r no0 ) = . (no2 lp p pr ) 6 r
extr {s ha
} (no2 lp p pu)
with extr
a pu
... | pr 6 r = rd (t2d (lp p pr )) 6 r
‘ ‘
extr {s ha
} (no3 lp p pq q qu) with extr qua
... | qr 6 r = t3r (lp p t2d (pq q qr )) 6 r
‘ ‘
‘ ‘
To delete the pivot key from between two trees, we extract the
rightmost key from the left tree, then weaken the bound on the right
tree (traversing its left spine only). Again, we are sure that if the
height remains the same, we shall deliver a 2-node.
delp : ∀{h } → [(L23 h ∧˙ L23 h) →
˙ Re2 h ]
delp {0} {lu } (no0 p no0 ) = trans>
⊥ {lu } (via p) ∴ / no0
‘ ‘
delp {s ha}
(lp p pu) with extr lp
‘ ‘
... | lr 6 r = d2t (lr r weak pu) where
‘ ‘
weak : ∀{h u } → L23 h (#p, u) → L23 h (#r, u)
weak {0} {u } no0 = trans>
⊥ { #r, u } (via p) ∴ no0
weak {s h } h pq q qu i = h weak pq q qu i
‘ ‘
‘ ‘
A remark on weakenings. It may seem regrettable that we have
to write weak, which is manifestly an obfuscated identity function,
and programmers who do not wish the ordering guarantees are
entitled not to pay and not to receive. If we took an extrinsic
approach to managing these invariants, weak would still be present,
but it would just be the proof of the proposition that you can
lower a lower bound that you know for a tree. Consequently, the
truly regrettable thing about weak is not that it is written but that
it is executed. What might help is some notion of ‘propositional
subtyping’, allowing us to establish coercions between types which
are guaranteed erasable at runtime because all they do is fix up
indexing and the associated content-free proof objects.
The completion of deleteion. Now that we can remove a key,
we need only find the key to remove. I have chosen to delete
the topmost occurrence of the given key, and to return the tree
unscathed if the key does not occur at all.
del23 : ∀{h } → [L• →
˙ L23 h →
˙ Del23 h ]
23
del {0}
no0
= . no0
del23 {s h } y◦ h lp p pu i
with decEq y p
‘ ‘
del23 {s h } .p◦ (no2 lp p pu)
| / hi
= rd (delp (lp p pu))
‘ ‘
del23 {s h } .p◦ (no3 lp p pq q qu) | / hi
= r3t (delp (lp p pq) q qu)
‘ ‘ ‘ ‘
del23 {s h } y◦ h lp p pu i
| . with owoto y p
‘ ‘
23
◦
del {s h } y (no2 lp p pu)
| . | le
= rd (d2t (del23 y◦ lp p pu))
‘ ‘
23
del {s h } y◦ (no2 lp p pu)
| . | ge
= rd (t2d (lp p del23 y◦ pu))
‘ ‘
del23 {s h } y◦ (no3 lp p pq q qu) | . | le
= r3t (d2t (del23 y◦ lp p pq) q qu)
‘ ‘ ‘ ‘
23
del {s h } y◦ (no3 lp p pq q qu) | . | ge with decEq y q
del23 {s h } .q◦ (no3 lp p pq q qu) | . | ge | / hi
= t3r (lp p delp (pq q qu))
‘ ‘
‘ ‘
... | . with owoto y q
... | le = r3t (t2d (lp p del23 y◦ pq) q qu)
‘ ‘
‘ ‘
... | ge = t3r (lp p t2d (pq q del23 y◦ qu))
‘ ‘
‘ ‘
As with insertion, the discipline of indexing by bounds and
height is quite sufficient to ensure that rebalancing works as required. The proof effort is just to reestablish the local ordering invariant around the deleted element.
At no point did we need to construct trees with the invariant
broken. Rather, we chose types which expressed with precision
the range of possible imbalances arising from a deletion. It is
exactly this precision which allowed us to build and justify the
rebalancing reconstruction operators we reused so effectively to
avoid an explosion of cases.
18.
Discussion
We have seen intrinsic dependently typed programming at work.
Internalizing ordering and balancing invariants to our datatypes,
we discovered not an explosion of proof obligations, but rather
that unremarkable programs check at richer types because they and
accountably do the testing which justifies their choices.
Of course, to make the programs fit neatly into the types, we
must take care of how we craft the latter. I will not pretend for
one moment that the good definition is the first to occur to me,
and it is certainly the case that one is not automatically talented at
desigining dependent types, even when one is an experienced pro-
grammer in Haskell or ML. There is a new skill to learn. Hopefully,
by taking the time to explore the design space for ordering invariants, I have exposed some transferable lessons. In particular, we
must overcome our type inference training and learn to see types as
pushing requirements inwards, as well as pulling guarantees out.
It is positive progress that work is shifting from the program
definitions to the type definitions, cashing out in our tools as considerable mechanical assistance in program construction. A precise
type structures its space of possible programs so tightly that an ineractive editor can often offer us a small choice of plausible alternatives, usually including the thing we want. It is exhilarating
being drawn to one’s code by the strong currents of a good design.
But that happens only in the last iteration: we are just as efficiently
dashed against the rocks by a bad design, and the best tool to support recovery remains, literally, the drawing board. We should give
more thought to machine-assisted exploration.
A real pleasure to me in doing this work was the realisation that
I not only had ‘a good idea for ordered lists’ and ‘a good idea for
ordered trees’, but that they were the same idea, and moreover that
I could implement the idea in a datatype-generic manner. The key
underpinning technology is first-class datatype description. By the
end of the paper, we had just one main datatype µ≤
IO , whose sole role
was to ‘tie the knot’ in a recursive node structure determined by a
computable code. The resulting raw data are strewn with artefacts
of the encoding, but pattern synonyms do a remarkably good job
of recovering the appearance of bespoke constructors whenever we
work specifically to one encoded datatype.
Indeed, there is clearly room for even more datatype-generic
technology in the developments given here. On the one hand,
the business of finding the substructure in which a key belongs,
whether for insertion or deletion, is crying out for a generic construction of G´erard Huet’s ‘zippers’ [Huet(1997)]. Moreover, the
treatment of ordered structures as variations on the theme of
the binary search tree demands consideration in the framework
´
of ‘ornaments’, as studied by Pierre-Evariste
Dagand and others [Dagand and McBride(2012)]. Intuitively, it seems likely that
the IO universe corresponds closely to the ornaments on nodelabelled binary trees which add only finitely many bits (because IO
has ‘+ rather than a general Σ). Of course, one node of a µ≤
IO type
corresponds to a region of nodes in a tree: perhaps ornaments, too,
should be extend to allow the unrolling of recursive structure.
Having developed a story about ordering invariants to the extent that our favourite sorting algorithms silently establish them, we
still do not have total correctness intrinsically. What about permutation? It has always maddened me that the insertion and flattening
operations manifestly construct their output by rearranging their input: the proof that sorting permutes should thus be by inspection.
Experiments suggest that many sorting algorithms can be expressed
in a domain specific language whose type system is linear for keys.
We should be able to establish a general purpose permutation invariant for this language, once and for all, by a logical relations
argument. We are used to making sense of programs, but it is we
who make the sense, not the programs. It is time we made programs
make their own sense.
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